Intelligent dynamic communication handoff for mobile applications

ABSTRACT

Microservices are predictively deployed on edge devices in a network. An application is run on a client device, the application comprising a set of microservices runnable on any edge device in a set of two or more edge devices. A state of the client device at a first time is determined, the state including one or more microservices currently being run for the client device, and for each microservice currently being run, an edge device running the microservice. One or more microservices that are likely to be run at a second time subsequent to the first time and a location of the client device at the second time are predicted. Based on the predicted location, a next edge device in the set of edge devices is determined for running the one or more microservices predicted to be run at the second time.

BACKGROUND

Embodiments relate generally to wireless communications, and moreparticularly to methods for predictively deploying softwaremicroservices to edge nodes (or edge devices) to enable rapidcommunication handoffs in a wireless network.

Wireless networking systems have become a prevalent means to communicatewith others worldwide and engage with content. Wireless communicationdevices, such as cellular telephones, tablet computers, and the likehave become smaller and more powerful in order to meet consumer needsand to improve portability and convenience. Consumers have becomedependent upon these devices, demanding reliable service, expanded areasof coverage, additional services (e.g., web browsing capabilities), andcontinued reduction in size and cost of such devices.

One of the most challenging requirements for a wireless network to beubiquitous is the ability to permit mobility, whenever and wherever,without loss of quality of service and connectivity. Such mobilitysupport allows users, with different mobility profiles, to traversedifferent geographical areas while continuing to access a variety ofmobile applications. A central component of supporting mobilitymanagement are handover mechanisms. In cellular telecommunications, theterms handover or handoff refer to the process of transferring anongoing call or data session from one channel connected to the corenetwork to another channel. In satellite communications, it is theprocess of transferring satellite control responsibility from one earthstation to another without loss or interruption of service. Handovermechanisms allow a user to change the physical point of attachmentwithin the mobile network when certain preprogrammed conditions aresatisfied. For example, if the received signal power from the currentbase station serving the user goes below a particular threshold and,simultaneously, the received signal power for another base stationnearby goes above a certain threshold, then a decision to change thepoint of attachment, i.e., the base station, can be made by the devicesin the network infrastructure in concert with the user device.

The worldwide mobile network infrastructure is in the midst of atransition to what is known as 5G, or fifth generation, technology. Ascompared to the current network scenario, the future 5G networkscenarios will be much more complex. Contributing towards this increasednetwork complexity will be the burgeoning demand for high quality databy users and devices, which are also expected to increase in numbersexponentially. There will also be technologically diverse applications,e.g., Virtual Reality (VR), Augmented Reality (AR), gaming, messaging,video playback, that go well beyond the traditional “voice and data”application commonly used today. There will be a variety of radio accesstechnologies, i.e., 5G New Radio, LTE, 3G, 2G, Wi-Fi, and a wide rangeof mobility profiles, including for example users on high speed trains,airplanes, cars or walking. The complexity of the 5G network is furtherexacerbated by all of these factors, not to mention the addition of newspectrum bands, e.g., millimeter wave frequencies, that will be utilizedand behave much differently than the traditional spectrum bands ofearlier mobile network generations.

In such an environment, existing static techniques for communicationhandoff may struggle with workload variances, resulting in loss ofcontinuity of computing and connectivity—and ultimately resulting inpoor user experience. For instance, in the current mobile networkscenario, the network has significant time to trigger, prepare, executeand complete a handover. However, the same is not true for the futurenetwork, where the density of access points will be high, i.e., accesspoints with smaller coverage areas and higher bandwidths will be packedmore closely in a given area. In addition, macro-cells withsignificantly large coverage areas will be present to assist the smallcells. If current handover mechanisms as described above were used, thetime to complete the handover could be much greater than the time theuser may be present at the desired base station while the conditions arestill favorable to establish a link. If the time available to performthe resource allocation and negotiation process is shorter, loss ofconnectivity is a higher risk with the established handover mechanisms,and this could result in poor network performance. The handoversignaling overhead will also be of critical importance in the context ofnetwork performance because of the failures caused by cell densificationand also the presence of diverse radio access technologies, i.e.,potentially many handoffs between 4G LTE and older technologies such asWi-Fi. Therefore, optimizing the handover process, where the latency andsignaling overhead are reduced, will be an extremely vital component offuture handover management strategies.

For example, consider the scenario of an AR gaming application thatrequires players to move from one location to another over a largephysical area equipped with AR markers while engaging in combatvirtually within the game. As players move quickly from one physicallocation to another, their communication with the gaming servers,whether the servers are in a central location (also known as the“cloud”) or closer to the user at the network edge, will need to beenabled via quick handoff from one network repeater to another. Asdescribed above, existing handover mechanisms may be too slow to handlethe dynamic nature of player movements and the corresponding need forhigh bandwidth and high computing resources.

The 5G wireless network also lends itself to what is known as an edgecomputing model. Edge computing is a distributed computing framework inwhich information processing is located close to the network edge, whichis where things and people produce or consume that information. Edgecomputing brings computation and data storage closer to the deviceswhere it's being gathered, rather than relying on a central locationthat can be thousands of miles away, resulting in a more decentralizedenvironment, just as described above for 5G wireless networks.

Edge computing was developed because of the exponential growth ofinternet-enabled devices such as autonomous vehicles or everyday deviceswithin the home such as security cameras or thermostats or, in acommercial setting, automated devices in a manufacturing line. Thesedevices now use the network for either receiving information from thecloud or delivering data back to the cloud. Many of these “edge devices”generate enormous amounts of data during the course of their operations.As examples, consider devices that monitor manufacturing equipment on afactory floor, or an Internet-connected video camera that sends livefootage from a remote office. While a single device producing data cantransmit it across a network quite easily, problems arise when thenumber of devices transmitting data at the same time grows. Instead ofone video camera transmitting live footage, hundreds or thousands ofdevices may be sending or receiving data, resulting in a potential lossof quality due to latency and tremendous bandwidth costs.

Edge computing hardware and services help solve this problem by being alocal source of processing and storage for many of these systems. Anedge server, for example, may process data from an edge device, and thensend only the relevant data back through the cloud, reducing bandwidthneeds, or it may send data back to the edge device in the case ofreal-time application needs. These edge devices may include manydifferent things, such as a smart thermostat, an employee's notebookcomputer, their latest smartphone, the security camera or even theinternet-connected microwave oven in the office break room. Edge serversthemselves and even the network repeaters that are installed in 5Gnetworks, e.g., cell towers, microcells, and Wi-Fi access points, may beconsidered edge devices within the edge computing infrastructure.

In tandem with the 5G wireless infrastructure and the edge computingmodel, a method of software development known as “microservicearchitecture” that has become popular in recent years also lends itselfto this decentralized environment. “Microservice architecture” refers toa particular way of designing software applications as suites ofindependently deployable microservices. These microservices run in theirown process and communicate with each other over a network tocollectively fulfill a goal using technology-agnostic and lightweightprotocols such as Hypertext Transfer Protocol (HTTP) with a bare minimumof centralized management. Microservices may be implemented usingdifferent programming languages, databases, or hardware and softwareenvironments, depending on what fits best for the specific microservice.Microservices may be small in size, messaging-enabled, autonomouslydeveloped, independently deployable, and built and released withautomated processes. Because of these characteristics, it is common formicroservices architectures to be adopted for cloud-native applications,serverless computing, and applications using lightweight containerdeployment, the very conditions that are prevalent in the 5G wirelessand edge computing environment.

SUMMARY

An embodiment is directed to a computer-implemented method forpredictively deploying microservices on edge devices in a network. Themethod may include running an application on a client device, theapplication comprising a set of microservices runnable on any edgedevice in a set of two or more edge devices.

The method may also include determining a state of the client device ata first time, where the state includes one or more microservicescurrently being run for the client device, and for each microservicecurrently being run, an edge device running the microservice. Thedetermined state of the first client device may be transmitted to atleast one edge device in the set of two or more edge devices.

In addition, the method may include predicting one or more microservicesthat are likely to be run at a second time subsequent to the first time.This may include determining, for each of the one or more microservicescurrently being run for the client device, a probability of amicroservice not currently being run for the client device beingrequested by the client device when the respective microservicecurrently being run concludes and ranking the microservices in order ofdetermined probability. A machine learning model may be used todetermine a probability that a microservice not currently being run forthe client device will be requested by the client device when amicroservice currently being run concludes.

The method may further include predicting a location of the clientdevice at the second time. Direction and speed of movement of the clientdevice may be determined and a location of the client device at thesecond time may be predicted based on the determined direction and speedof movement of the client device.

The method may also include determining, based on the predictedlocation, a next edge device in the set of edge devices for running theone or more microservices predicted to be run at the second time. Afirst edge device as a candidate next edge device may be evaluated bydetermining a compute capacity of the first edge device, determining aworkload of the first edge device, predicting a level of operationalservice if the predicted one or more microservices that are likely to berun are run at the second time on the first edge device and selectingthe first edge device as the next edge device if the level ofoperational service is at or above a threshold level of operationalservice. In an embodiment, the first edge device may not be selected asthe next edge device if the level of operational service is below athreshold level of operational service and a second edge device may beevaluated as the candidate next edge device.

Lastly, the method may include determining a time required to deploy theone or more microservices predicted to be run on the determined nextedge device at the second time and initiating a deployment, at a thirdtime, on the determined next edge device of the one or moremicroservices predicted to be run on the next edge device, the thirdtime being prior to the second time, wherein the third time isdetermined based on the determined time required to deploy the one ormore microservices predicted to be run on the determined next edgedevice. A compute capacity and workload of the determined next edgedevice may also be determined.

In addition to a computer-implemented method, additional embodiments aredirected to a system and a computer program product for predictivelydeploying microservices on edge devices in a network.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example computer system in which variousembodiments may be implemented.

FIG. 2 depicts an example microservices interaction diagram according toan embodiment.

FIG. 3 depicts an example mobile wireless network with a client devicetraveling between edge devices according to an embodiment.

FIG. 4 is a flow chart diagram of a microservice prediction process in amobile wireless network architecture in accordance with one or moreembodiments.

FIG. 5 shows a block diagram of the inputs and machine learning model ofa microservice prediction module for determining the likelihood of amicroservice being needed next in a mobile wireless network.

FIG. 6 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 7 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

In an example wireless environment, there may be a number of softwareapplications to be supported by the network infrastructure (repeatersand servers), with each application comprising of a set ofmicroservices. In this scenario, it is important to have themicroservices deployed and ready to run on the appropriate edge device(in this case, network repeater) at the time they are needed to processthe data from the users. The edge device has limited computingcapability so it is not feasible to have all the microservices of anapplication deployed and running at all edge devices. In order to meetthe needs of all the users being served by the network, it is helpful toidentify which microservices are deployed on which edge devices and atwhat time. It takes some time for a microservice to be deployed on anedge device and that time must be taken into account in determining whenthe microservice needs to be ready. There is a need to determine aprobability that, given a number of network repeaters or edge deviceswith compute and bandwidth capacity, a specific microservice is likelyto be needed next at a specific edge device.

Referring now to FIG. 1, there is shown a block diagram illustrating acomputer system 100 which may be embedded in an edge server in anembodiment. In another embodiment, the computer system 100 may beembedded in a network repeater such as a cell tower or 5G microcell. Inyet another embodiment, the computer system 100 may be embedded in aclient device or mobile client device, examples of which include: amobile phone, smart phone, tablet, laptop, a computing device embeddedin a vehicle, a wearable computing device, virtual or augmented realityglasses or headset, and the like. As shown, a computer system 100includes a processor unit 102, a memory unit 104, a persistent storage106, a communications unit 112, an input/output unit 114, a display 116,and a system bus 110. Computer programs such as the microserviceprediction module 120 may be stored in the persistent storage 106 untilthey are needed for execution, at which time the programs are broughtinto the memory unit 104 so that they can be directly accessed by theprocessor unit 102. The processor unit 102 selects a part of memory unit104 to read and/or write by using an address that the processor 102gives to memory 104 along with a request to read and/or write. Usually,the reading and interpretation of an encoded instruction at an addresscauses the processor 102 to fetch a subsequent instruction, either at asubsequent address or some other address. The processor unit 102, memoryunit 104, persistent storage 106, communications unit 112, input/outputunit 114, and display 116 interface with each other through the systembus 110.

Referring to FIG. 2, a diagram depicting an example microservicesinteraction is shown that will be used in conjunction with FIG. 4 as anexample illuminating the process. In this example, microservices 202-222of an example application may initially run in a particular order butafter an initial period, different microservices may get triggered atdifferent points based on the actions of the users so there is no uniqueorder for how the microservices get triggered. The microserviceprediction module 120 may predict which microservice(s) are most likelyto be needed at a given edge device D at time T and may start the act ofdeploying such microservices so they are ready for execution whenneeded.

Referring to FIG. 3, an example mobile wireless network is depicted.Client device 310 is shown within the range 306 and communicating withthe current edge device 302 while moving in the direction 320 into therange 308 of the next edge device 304. While a mobile phone is depictedas an example of a client device in FIG. 3, it should be appreciatedthat client device 310 may be any mobile device, such as a mobile phone,smart phone, tablet, laptop, a computing device embedded in a vehicle, awearable computing device, virtual or augmented reality glasses orheadset. In addition, while edge nodes 302, 304 are depicted as celltowers, it should be appreciated that a vehicle, such as a vehiclehaving a containerized entertainment system, may be an edge node. As theclient device 310 moves out of range 306 of the current edge device 302and into the range 308 of the next edge device 304, the current edgedevice 302 will hand off communications to the next edge device 304. Thecurrent edge device 302 and the next edge device 304 are said to be“neighboring” edge devices. In this example, the microservices that arecalculated by the microservice prediction module 120 to be likely to beneeded next may be deployed on the next edge device 304 in advance ofthe handover. It should be appreciated by one of skill in the art thatfor simplicity of illustration, FIG. 3 shows only one current edgedevice, one next edge device and a single client device moving in asingle direction but in practice, a single edge device may serviceseveral client devices at once that are moving in more than onedirection and a single current edge device may have multiple neighboringedge devices that will act as the next edge device.

Referring to FIG. 4, a flow chart of a prediction process 400 fordeploying the needed microservices is depicted. In various embodiments,some or all of the operations of this process may be performed by anedge server or edge device. In other embodiments, some or all of theoperations of this process may be performed by a mobile networkrepeater.

At 402, the microservice prediction module 120 may determine anddistribute (or transmit) to all edge devices (D) a state (ST) for eachclient device (i), where the state captures which edge device the clientdevice is currently connected to and which microservice(s) the clientdevice is currently running, e.g., at a first time. Referring to themicroservices shown in the example of FIG. 2, and given N clientdevices, the state of client device i=1 is ST1={D1,202}, which indicatesthat client device 1 is currently connected to edge device D1 andcurrently running microservice 202 (which could be an authenticationmicroservice as an example). The state of client device i=2 isST2={D4,202}, which indicates that client device 2 is currentlyconnected to device D4 and is currently running microservice 202. Itshould be noted that this example references one microservice per userstate for simplicity, but the same edge device may run multiplemicroservices for a single client device per state, e.g., ST3={D1, 206,216}. In addition, a single edge device may simultaneously run the samemicroservice for two or more client devices, e.g., ST1={D1, 202} andST5={D1, 202}.

At 404, the microservice prediction module 120 may collect fromneighboring edge devices which client devices they are serving. Theresult would be a list of client devices and their corresponding states.For example, the list of client devices for an edge device Dj may be{U1, U4, U8, U9, . . . } and the corresponding states for these clientdevices on edge device Dj may be {ST1, ST4, ST8, ST9, . . . }. Fromthese lists of client devices served at the neighboring edge devices andtheir states, the microservice prediction module 120 may extract themicroservices currently being run, e.g., at the first time. For example,if the state for client device 1 indicates microservice 206 and thestate for client device 2 indicates microservice 204, then the list ofmicroservices being run on that edge device includes 204, 206 and so on.A sample list of microservices being run on edge device Dj may be {204,206, 210, 206, 204, 214, . . . }. It should be noted that the samemicroservice may show up multiple times, i.e., if different clientdevices are served by neighboring devices running the same microservice.

At 406, the microservice prediction module 120 may identify (or predict)the microservices likely to be run next based on the list ofmicroservices currently being run. Initially, this may be based on afixed configuration setting but subsequently and over time, theprediction may be refined according to actual execution, as describedbelow in 410. As an example, the list of microservices currently runningmay include 3 occurrences (or instances) of 204 (corresponding to 3client devices currently using 204), 1 occurrence of 206, and 5occurrences of 214. In this case, the microservice prediction module 120may determine that the next most likely microservices are 222 and 204each with probability p=(5/9)*(1/2), where 5/9 is based on the number ofoccurrences of 214 and 1/2 is based on the example of FIG. 2, where theinitial assumption is that 222 or 204 will run after 214 with equalprobability. Also in this list of most likely microservices to run nextmay be 206 with probability p=(3/9)*1, where 3/9 is based on the numberof occurrences of 204 and 1 is based on the example of FIG. 2, whereonly 206 is run after 204, and 208 with probability p=(1/9)*1, where 1/9is based on the number of occurrences of 206 and 1 is based on theexample of FIG. 2, where only 208 is run after 206.

In addition to predicting the likely microservices to be run next atstep 406, the microservice prediction module 120 may also predict alikely location of the client device by determining the direction andspeed of movement of the client device (or a vehicle in which a user ofthe client device is located) in order to select a suitable next edgedevice 304. The microservice prediction module 120 also predicts thetime at which the client device 310 is predicted to be withincommunication range (or handoff range) of the suitable next edge device304 based on the direction and speed of movement of the client device.Accordingly, the microservice prediction module 120 may predict one ormore microservices that are likely to be run at a second time subsequentto the first time and a location of the client device at the secondtime. Further, the microservice prediction module 120 may determine,based on the predicted location, a next edge device in the set of edgedevices for running a microservice predicted to be run at the secondtime, and may determine a time required to deploy the microservicepredicted to be run on the determined next edge device at the secondtime.

At 408, the microservice prediction module 120 may deploy microserviceson a next edge device 304 in accordance with the predictions of theprevious step and consistent with the potential computing or memory orbandwidth limitations of the edge device that are calculated at thisstep. Each next edge device 304 may compute the start-up time requiredto load each individual microservice, which may differ by edge devicesince edge devices may be heterogeneous, both in terms of hardware andsoftware, and workloads. For instance, the time that is required for aspecific microservice to load on a cell tower may be very different fromthat of a 5G microcell. Different edge devices may have different typesand numbers of processors, different amounts and types of memory, anddifferent types and versions of software. Moreover, the startup time fora particular microservice on a particular edge device at a particulartime may depend on the amount of other processing currently beingperformed on the edge device at that time. Start-up time also depends onattributes of the particular microservice, such as size of the code andlibraries required. The computation of startup time may account for someor all of these factors. A table such as the following may be computed:

Microservice Edge Device Start-up Time 202 D1 20 msec 204 D1 40 msec 204D4 35 msec 208 D3 20 msec

Once the microservice prediction module 120 has computed a startup time,it may initiate the deployment of the microservice at an appropriatetime so that the microservice is available in time for a potentialhandover of the communication session from the current edge device 302to the next edge device 304. Accordingly, deployment of a microservicepredicted to be run may be initiated at a third time on a next edgedevice (the third time being prior to the second time and determinedbased on the time required to deploy the microservice predicted to berun on the next edge device). In the example above, microservice 204 hasa startup time of 40 msec on edge device D1 so if microservice 204 ispredicted to be needed next and the next edge device 304 is determinedto be edge device D1, the microservice prediction module 120 mustinitiate deployment of microservice 204 on edge device D1 at least 40msec in advance of the client device 310 reaching next edge device 304(which is also edge device D1 in the example). As mentioned, the time atwhich the client device 310 is predicted to reach or be within hand offrange of a next edge device 304 may be determined at 406.

In addition, if it were determined that the next edge device 304 iscapable of deploying 10 microservices, the next edge device 304 woulddeploy the 10 most likely to be needed. In the example environment ofFIG. 2, the system would deploy 3 instances of 222 and 3 instances of204 to serve the 5 client devices currently using 214, 3 instances of206 to serve the client devices currently using 204 and one instance of208 to serve the client device currently using 206. It should beappreciated by one of skill in the art that for simplicity ofillustration, this example assumes a one-to-one correlation betweeninstance of microservices and users but in practice, one instance of amicroservice may service a large number of users. In another embodiment,an optional step may be added to handle cases when a device can onlysupport a number of microservices that is not adequate to serve theneeds for all users connected to the device. For example, the predictionmay determine that an edge device needs to deploy 15 microservices asthey are most likely to be needed by all client devices but the edgedevice only has capacity for 10. In this case, the remaining 5microservices may be dispatched to other neighboring edge devices whichcould support the client devices and a prioritization or assignmentscheme may be proposed to handle the microservices in that scenario. Inthis alternative, the startup time and the time at which the clientdevice 310 is predicted to reach or be within hand off range of theneighboring edge device 304 may be determined as described above.

At 410, the microservice prediction module 120 may collect historicaldata about the client devices that are connected to the next edge device304 and the microservices that are running on the next edge device andutilize a supervised machine learning model to develop a profile of themost likely microservices to run next and refine its prediction asdescribed further in FIG. 5.

Referring to FIG. 5, a diagram showing examples of components or modulesof a microservice prediction process according to at least oneembodiment. According to one embodiment, the process may includemicroservice prediction module 120 which utilizes supervised machinelearning 520 to determine the likelihood of a microservice to be needednext. The supervised machine learning model may use an appropriatemachine learning algorithm, e.g., Support Vector Machines (SVM) orrandom forests. The microservice prediction module 120 monitors theactual execution of microservices as described in 410 and tracks theclient device information 502, i.e., both the list of currentlyconnected client devices from the neighboring edge devices and the listof client devices currently connected to the next edge device 404 andmay also analyze current state information 504, i.e., the state that isreceived by all edge devices in 402. The microservice prediction module120 may use the above information to determine the likelihood ofexecution 510 that a microservice will be needed next and updates theprobabilities that are initially determined from the configuration filein step 210 above. For example, it may be discovered that users choose222 following 214 80% of the time (as opposed to the 50% probability inthe configuration file) and the microservice prediction module 120 mayadjust to that change and update its prediction, or probability oflikelihood, for the next microservice that is needed to run.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 7, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66, such as a load balancer. In some embodiments,software components include network application server software 67 anddatabase software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and other applications 96 such as themicroservice prediction module 120.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method for predictivelydeploying microservices on edge devices in a network, the methodcomprising: running an application on a client device, the applicationcomprising a set of microservices runnable on any edge device in a setof two or more edge devices; determining a state of the client device ata first time, the state including one or more microservices currentlybeing run for the client device, and for each microservice currentlybeing run, an edge device running the microservice; predicting one ormore microservices that are likely to be run at a second time subsequentto the first time; predicting a location of the client device at thesecond time; and determining, based on the predicted location, a nextedge device in the set of edge devices for running the one or moremicroservices predicted to be run at the second time.
 2. Thecomputer-implemented method of claim 1, wherein the determining a stateof the client device at a first time further comprises: transmitting thedetermined state of the first client device to at least one edge devicein the set of two or more edge devices.
 3. The computer-implementedmethod of claim 1, wherein the predicting one or more microservices thatare likely to be run at a second time subsequent to the first timefurther comprises: determining, for each of the one or moremicroservices currently being run for the client device, a probabilityof a microservice not currently being run for the client device beingrequested by the client device when the respective microservicecurrently being run concludes; and ranking the microservices in order ofdetermined probability.
 4. The computer-implemented method of claim 3,wherein the determining, for each of the one or more microservicescurrently being run for the client device, a probability of amicroservice not currently being run for the client device beingrequested by the client device when the respective microservicecurrently being run concludes further comprises: using a machinelearning model to determine a probability that a microservice notcurrently being run for the client device will be requested by theclient device when a microservice currently being run concludes.
 5. Thecomputer-implemented method of claim 1, wherein the predicting alocation of the client device at the second time further comprises:determining direction and speed of movement of the client device; andpredicting a location of the client device at the second time based onthe determined direction and speed of movement of the client device. 6.The computer-implemented method of claim 1, wherein the determining,based on the predicted location, a next edge device in the set of edgedevices for running the one or more microservices predicted to be run atthe second time further comprises: evaluating a first edge device as acandidate next edge device by: determining a compute capacity of thefirst edge device; determining a workload of the first edge device;predicting a level of operational service if the predicted one or moremicroservices that are likely to be run are run at the second time onthe first edge device; and selecting the first edge device as the nextedge device if the level of operational service is at or above athreshold level of operational service.
 7. The computer-implementedmethod of claim 6, further comprising: not selecting the first edgedevice as the next edge device if the level of operational service isbelow a threshold level of operational service; and evaluating a secondedge device as the candidate next edge device.
 8. Thecomputer-implemented method of claim 1, further comprising: determininga time required to deploy the one or more microservices predicted to berun on the determined next edge device at the second time; andinitiating a deployment, at a third time on the determined next edgedevice, of the one or more microservices predicted to be run on the nextedge device, the third time being prior to the second time, wherein thethird time is determined based on the determined time required to deploythe one or more microservices predicted to be run on the determined nextedge device.
 9. The computer-implemented method of claim 8, wherein thedetermining a time required to deploy the one or more microservicespredicted to be run on the determined next edge device at the secondtime further comprises: determining a compute capacity of the determinednext edge device; and determining a workload of the determined next edgedevice.
 10. A computer system for predictively deploying microserviceson edge devices in a network comprising: one or more processors, one ormore computer-readable memories, one or more computer-readable tangiblestorage media, and program instructions stored on at least one of theone or more tangible storage media for execution by at least one of theone or more processors via at least one of the one or more memories,wherein the computer system is capable of performing a methodcomprising: running an application on a client device, the applicationcomprising a set of microservices runnable on any edge device in a setof two or more edge devices; determining a state of the client device ata first time, the state including one or more microservices currentlybeing run for the client device, and for each microservice currentlybeing run, an edge device running the microservice; predicting one ormore microservices that are likely to be run at a second time subsequentto the first time; predicting a location of the client device at thesecond time; and determining, based on the predicted location, a nextedge device in the set of edge devices for running the one or moremicroservices predicted to be run at the second time.
 11. The computersystem of claim 10, wherein the determining a state of the client deviceat a first time further comprises: transmitting the determined state ofthe first client device to at least one edge device in the set of two ormore edge devices.
 12. The computer system of claim 10, wherein thepredicting one or more microservices that are likely to be run at asecond time subsequent to the first time further comprises: determining,for each of the one or more microservices currently being run for theclient device, a probability of a microservice not currently being runfor the client device being requested by the client device when therespective microservice currently being run concludes; and ranking themicroservices in order of determined probability.
 13. The computersystem of claim 12, wherein the determining, for each of the one or moremicroservices currently being run for the client device, a probabilityof a microservice not currently being run for the client device beingrequested by the client device when the respective microservicecurrently being run concludes further comprises: using a machinelearning model to determine a probability that a microservice notcurrently being run for the client device will be requested by theclient device when a microservice currently being run concludes.
 14. Thecomputer system of claim 10, wherein the predicting a location of theclient device at the second time further comprises: determiningdirection and speed of movement of the client device; and predicting alocation of the client device at the second time based on the determineddirection and speed of movement of the client device.
 15. The computersystem of claim 10, wherein the determining, based on the predictedlocation, a next edge device in the set of edge devices for running theone or more microservices predicted to be run at the second time furthercomprises: evaluating a first edge device as a candidate next edgedevice by: determining a compute capacity of the first edge device;determining a workload of the first edge device; predicting a level ofoperational service if the predicted one or more microservices that arelikely to be run are run at the second time on the first edge device;and selecting the first edge device as the next edge device if the levelof operational service is at or above a threshold level of operationalservice.
 16. The computer system of claim 15, further comprising: notselecting the first edge device as the next edge device if the level ofoperational service is below a threshold level of operational service;and evaluating a second edge device as the candidate next edge device.17. The computer system of claim 10, further comprising: determining atime required to deploy the one or more microservices predicted to berun on the determined next edge device at the second time; andinitiating a deployment, at a third time on the determined next edgedevice, of the one or more microservices predicted to be run on the nextedge device, the third time being prior to the second time, wherein thethird time is determined based on the determined time required to deploythe one or more microservices predicted to be run on the determined nextedge device.
 18. The computer-implemented method of claim 17, whereinthe determining a time required to deploy the one or more microservicespredicted to be run on the determined next edge device at the secondtime further comprises: determining a compute capacity of the determinednext edge device; and determining a workload of the determined next edgedevice.
 19. A computer program product for predictively deployingmicroservices on edge devices in a network comprising: a computerreadable storage device storing computer readable program code embodiedtherewith, the computer readable program code comprising program codeexecutable by a computer to perform a method comprising: running anapplication on a client device, the application comprising a set ofmicroservices runnable on any edge device in a set of two or more edgedevices; determining a state of the client device at a first time, thestate including one or more microservices currently being run for theclient device, and for each microservice currently being run, an edgedevice running the microservice; predicting one or more microservicesthat are likely to be run at a second time subsequent to the first time;predicting a location of the client device at the second time; anddetermining, based on the predicted location, a next edge device in theset of edge devices for running the one or more microservices predictedto be run at the second time.
 20. The computer program product of claim19, wherein the determining a state of the client device at a first timefurther comprises: transmitting the determined state of the first clientdevice to at least one edge device in the set of two or more edgedevices.
 21. The computer program product of claim 19, wherein thepredicting one or more microservices that are likely to be run at asecond time subsequent to the first time further comprises: determining,for each of the one or more microservices currently being run for theclient device, a probability of a microservice not currently being runfor the client device being requested by the client device when therespective microservice currently being run concludes; and ranking themicroservices in order of determined probability.
 22. The computerprogram product of claim 21, wherein the determining, for each of theone or more microservices currently being run for the client device, aprobability of a microservice not currently being run for the clientdevice being requested by the client device when the respectivemicroservice currently being run concludes further comprises: using amachine learning model to determine a probability that a microservicenot currently being run for the client device will be requested by theclient device when a microservice currently being run concludes.
 23. Thecomputer program product of claim 19, wherein the predicting a locationof the client device at the second time further comprises: determiningdirection and speed of movement of the client device; and predicting alocation of the client device at the second time based on the determineddirection and speed of movement of the client device.
 24. The computerprogram product of claim 1, wherein the determining, based on thepredicted location, a next edge device in the set of edge devices forrunning the one or more microservices predicted to be run at the secondtime further comprises: evaluating a first edge device as a candidatenext edge device by: determining a compute capacity of the first edgedevice; determining a workload of the first edge device; predicting alevel of operational service if the predicted one or more microservicesthat are likely to be run are run at the second time on the first edgedevice; and selecting the first edge device as the next edge device ifthe level of operational service is at or above a threshold level ofoperational service.
 25. The computer program product of claim 24,further comprising: not selecting the first edge device as the next edgedevice if the level of operational service is below a threshold level ofoperational service; and evaluating a second edge device as thecandidate next edge device.